Metagenome-atlas output summary

Atlas output files are stored in the Example folder in the GitHub repository metagenome-atlas/Tutorial.

If you don’t already have a copy of the GitHub repository locally, you can run the following command from a Terminal. Note that RStudio includes a Terminal tab next to the Console tab. In the default configuration of RStudio, this panel is located in the bottom left hand corner.

git clone https://github.com/metagenome-atlas/Tutorial.git

library(dplyr)
library(readr)
library(stringr)
library(tidyr)
library(tibble)
library(ggplot2)
library(plotly)
library(pheatmap)
library(microbiome)
library(ape)
library(vegan)
library(useful)
library(kableExtra)

Metagenome assembled genomes

Taxonomy

Tax <- read_tsv("Example/Results/taxonomy.tsv")
kable(Tax)

user_genome	kindom	phylum	class	order	family	genus	species
MAG11	Bacteria	Firmicutes	Bacilli	Lactobacillales	Lactobacillaceae	Limosilactobacillus	Limosilactobacillus reuteri
MAG30	Bacteria	Firmicutes	Bacilli	Lactobacillales	Lactobacillaceae	Ligilactobacillus	Ligilactobacillus murinus
MAG08	Bacteria	Firmicutes	Bacilli	Lactobacillales	Lactobacillaceae	Lactobacillus	Lactobacillus johnsonii
MAG40	Bacteria	Firmicutes	Bacilli	Erysipelotrichales	Erysipelotrichaceae	Faecalibaculum	Faecalibaculum rodentium
MAG42	Bacteria	Firmicutes	Bacilli	Erysipelotrichales	Erysipelatoclostridiaceae	Erysipelatoclostridium	Erysipelatoclostridium cocleatum
MAG20	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	Kineothrix	Kineothrix sp000403275
MAG03	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	CAG-95	CAG-95 sp000403495
MAG33	Bacteria	Actinobacteriota	Coriobacteriia	Coriobacteriales	Atopobiaceae	NM07-P-09	NM07-P-09 sp004793925
MAG39	Bacteria	Proteobacteria	Gammaproteobacteria	Burkholderiales	Burkholderiaceae	Parasutterella	Parasutterella sp900552195
MAG49	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Bacteroidaceae	Bacteroides	Bacteroides intestinalis
MAG45	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Bacteroidaceae	Phocaeicola	Phocaeicola vulgatus
MAG58	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Bacteroidaceae	Phocaeicola	Phocaeicola sp002493165
MAG10	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Muribaculaceae	UBA7173	UBA7173 sp001689485
MAG35	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Muribaculaceae	UBA7173	UBA7173 sp001915385
MAG48	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Muribaculaceae	Paramuribaculum	Paramuribaculum sp900553585
MAG02	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Muribaculaceae	CAG-485	CAG-485 sp002361155
MAG56	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Tannerellaceae	Parabacteroides	Parabacteroides goldsteinii
MAG38	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	UBA932	RC9	RC9 sp002298075
MAG44	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Rikenellaceae	Alistipes	Alistipes sp002428825
MAG24	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Rikenellaceae	Alistipes	Alistipes sp002362235
MAG16	Bacteria	Verrucomicrobiota	Verrucomicrobiae	Verrucomicrobiales	Akkermansiaceae	Akkermansia	Akkermansia muciniphila
MAG51	Bacteria	Deferribacterota	Deferribacteres	Deferribacterales	Mucispirillaceae	Mucispirillum	Mucispirillum schaedleri
MAG12	Bacteria	Firmicutes_A	Clostridia	Oscillospirales	Acutalibacteraceae	Eubacterium_R	NA
MAG19	Bacteria	Firmicutes_A	Clostridia	Oscillospirales	Ruminococcaceae	Angelakisella	NA
MAG41	Bacteria	Firmicutes_A	Clostridia	Oscillospirales	Oscillospiraceae	NA	NA
MAG63	Bacteria	Firmicutes_A	Clostridia	Oscillospirales	Oscillospiraceae	NA	NA
MAG21	Bacteria	Firmicutes_A	Clostridia	Oscillospirales	Oscillospiraceae	NA	NA
MAG59	Bacteria	Firmicutes_A	Clostridia	Oscillospirales	Oscillospiraceae	NA	NA
MAG46	Bacteria	Firmicutes_A	Clostridia	Oscillospirales	Oscillospiraceae	UBA9475	NA
MAG50	Bacteria	Firmicutes_A	Clostridia	Oscillospirales	Oscillospiraceae	UBA9475	NA
MAG18	Bacteria	Firmicutes_A	Clostridia	Oscillospirales	Oscillospiraceae	Lawsonibacter	NA
MAG15	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	UBA7182	NA
MAG06	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	NA	NA
MAG07	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	Dorea	NA
MAG36	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	Dorea	NA
MAG05	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	An181	NA
MAG32	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	1XD42-69	NA
MAG28	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	1XD42-69	NA
MAG53	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	14-2	NA
MAG04	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	NA	NA
MAG23	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	Kineothrix	NA
MAG13	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	NA	NA
MAG55	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	1XD8-76	NA
MAG43	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	CAG-95	NA
MAG31	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	Acetatifactor	NA
MAG01	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	Acetatifactor	NA
MAG47	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	Acetatifactor	NA
MAG52	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	Acetatifactor	NA
MAG37	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	Acetatifactor	NA
MAG17	Bacteria	Firmicutes_A	Clostridia	Lachnospirales	Lachnospiraceae	Acetatifactor	NA
MAG22	Bacteria	Firmicutes_A	Clostridia_A	Christensenellales	UBA3700	NA	NA
MAG27	Bacteria	Firmicutes_A	Clostridia_A	Christensenellales	UBA3700	NA	NA
MAG29	Bacteria	Cyanobacteria	Vampirovibrionia	Gastranaerophilales	Gastranaerophilaceae	Zag111	NA
MAG26	Bacteria	Proteobacteria	Alphaproteobacteria	Rs-D84	Rs-D84	Rs-D84	NA
MAG54	Bacteria	Desulfobacterota	Desulfovibrionia	Desulfovibrionales	Desulfovibrionaceae	Mailhella	NA
MAG57	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Muribaculaceae	CAG-873	NA
MAG25	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Muribaculaceae	Paramuribaculum	NA
MAG61	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Muribaculaceae	Paramuribaculum	NA
MAG60	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Muribaculaceae	Paramuribaculum	NA
MAG09	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Muribaculaceae	CAG-485	NA
MAG14	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Muribaculaceae	CAG-485	NA
MAG34	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Muribaculaceae	CAG-485	NA
MAG62	Bacteria	Bacteroidota	Bacteroidia	Bacteroidales	Marinifilaceae	Odoribacter	NA

# create a short label for each species
Tax <- Tax %>%
  mutate(Labels = ifelse(is.na(species) & is.na(genus), paste0(family, " ", user_genome), species)) %>%
  mutate(Labels = ifelse(is.na(Labels), paste0(genus, " ", user_genome), Labels))

Genome quality

genome_quality <- read_tsv("Example/Results/genome_completeness.tsv") %>%
  mutate(Quality_Score = Completeness - (5*Contamination)) %>%
  mutate(Lineage = sapply(str_split(`Marker lineage`, " "), function(x) x[1])) %>%
  left_join(Tax, by = c("Bin Id" = "user_genome")) %>%
  mutate(Name = Labels) %>%
  select(-Labels)

plt <- ggplot(genome_quality, aes(x = Contamination, y = Completeness, color = phylum)) +
  geom_point() +
  theme_minimal()
ggplotly(plt)

Abundance

Counts <- read_tsv("Example/Results/counts/raw_counts_genomes.tsv")
kable(topleft(Counts, c = 10))

Sample	ERR675518	ERR675519	ERR675520	ERR675521	ERR675522	ERR675523	ERR675524	ERR675525	ERR675526
MAG01	17393	281563	26242	331737	23531	269746	23891	173870	44879
MAG02	117204	34570	86293	36247	91372	47348	114237	36185	124830
MAG03	240075	29109	109919	40084	35514	47457	222384	33715	269218
MAG04	24199	726600	18037	747249	73140	290866	13242	339349	29056
MAG05	97526	201572	104119	254551	36218	484727	61292	375456	191088

Mapping rate

mapping_rate <- read_tsv("Example/Results/mapping_rate.tsv", 
                         col_names = c("Sample", "Mapping_rate"), skip = 1)  %>%
  mutate(tmp = "samples")

ggplot(mapping_rate, aes(x = tmp, y = Mapping_rate)) +
  geom_jitter() +
  ylim(c(0, 1))+
  theme_minimal()

Stats based on raw counts

There are good reasons to use rawcounts and use centric log ratios, see more in

Gloor, Gregory B., Jean M. Macklaim, Vera Pawlowsky-Glahn, and Juan J. Egozcue. 2017. “Microbiome Datasets Are Compositional: And This Is Not Optional.” Frontiers in Microbiology 8 (November). Frontiers: 2224. doi:10.3389/fmicb.2017.02224.

For differencial abundance analysis see also the same paper.

# transform counts with centered log ratio
data <- Counts %>%
  column_to_rownames(var = "Sample") %>%
  as.data.frame()
data <- transform(data, "clr")

pheatmap(data, cluster_rows = T, cluster_cols = T,
         labels_row = Tax$Labels)

PCA (PCoA) of the robust aitchison distance

pcoa_data <- pcoa(dist(t(data), method = "euclidean"))
pcoa_df <- as.data.frame(pcoa_data$vectors)

plt <- ggplot(pcoa_df, aes(x = Axis.1, y = Axis.2, label = rownames(pcoa_df))) +
  geom_point() +
  labs(x = paste0("PC1 (", round(pcoa_data$values$Relative_eig[1] * 100, digits = 1), "%)"),
       y = paste0("PC2 (", round(pcoa_data$values$Relative_eig[2] * 100, digits = 1), "%)")) +
  theme_minimal()

ggplotly(plt)

Relative abundance

For the relative abundance we take the coverage over the genome not the raw counts. This inmplicit normalizes for genome size. The coverage is calculated as the median of the coverage values calculated in 1kb blocks.

D <- read_tsv("Example/Results/counts/median_coverage_genomes.tsv") %>%
  column_to_rownames(var = "X1") %>%
  as.data.frame()
kable(head(D))

	MAG01	MAG02	MAG03	MAG04	MAG05	MAG06	MAG07	MAG08	MAG09	MAG10	MAG11	MAG12	MAG13	MAG14	MAG15	MAG16	MAG17	MAG18	MAG19	MAG20	MAG21	MAG22	MAG23	MAG24	MAG25	MAG26	MAG27	MAG28	MAG29	MAG30	MAG31	MAG32	MAG33	MAG34	MAG35	MAG36	MAG37	MAG38	MAG39	MAG40	MAG41	MAG42	MAG43	MAG44	MAG45	MAG46	MAG47	MAG48	MAG49	MAG50	MAG51	MAG52	MAG53	MAG54	MAG55	MAG56	MAG57	MAG58	MAG59	MAG60	MAG61	MAG62	MAG63
ERR675518	0.00	6.020	6.47	0.24	1.82	0.02	0.16	0.50	104.515	41.08	0.00	0.00	0.00	3.73	0.00	117.710	1.56	0.99	0.66	8.965	0.16	19.32	0.00	10.930	8.040	2.370	0.00	0.160	6.81	1.620	0.160	0.515	22.985	36.40	146.48	1.040	0.00	40.30	5.65	138.785	1.08	0.835	0.14	1.93	16.11	0.94	0.16	8.67	31.62	0.00	6.005	0.31	2.200	33.375	15.760	2.67	17.44	42.300	0.31	1.41	21.75	7.375	0.57
ERR675519	5.59	1.320	0.00	10.47	3.36	19.18	2.53	41.03	0.000	3.83	30.50	8.34	0.00	0.00	3.34	140.215	0.00	4.07	9.95	78.820	0.00	0.00	0.00	4.890	9.960	2.610	0.00	3.890	0.00	3.230	46.235	9.775	34.750	0.00	0.00	4.340	0.50	45.45	0.00	125.305	5.92	9.875	0.00	0.19	4.83	0.38	10.36	0.00	0.41	11.51	2.880	6.07	6.850	42.480	0.620	0.96	0.21	44.725	10.14	0.00	0.31	10.930	0.24
ERR675520	0.22	4.950	2.58	0.12	1.34	0.09	0.41	0.00	72.945	34.41	0.00	6.74	7.29	3.00	0.00	103.405	0.00	1.19	0.76	0.900	0.00	7.39	11.20	4.435	23.900	30.975	9.10	0.000	0.00	3.180	0.000	0.000	0.000	30.25	214.27	1.120	0.78	12.14	0.00	49.975	1.85	0.300	0.08	1.31	30.66	2.41	0.49	55.51	78.25	0.00	3.940	0.66	2.850	17.345	16.875	3.22	7.94	0.170	0.34	3.02	0.24	8.275	2.65
ERR675521	5.82	1.720	0.00	12.07	3.21	21.45	5.16	25.56	1.090	4.34	35.06	6.23	0.00	0.00	8.10	70.815	0.00	4.16	12.45	64.925	2.73	0.00	0.00	12.590	11.130	1.370	0.00	2.870	0.00	6.965	39.315	24.785	0.000	0.00	0.00	3.890	15.40	25.81	0.00	56.320	4.11	3.835	20.98	0.81	14.38	3.20	9.22	8.52	0.81	42.66	4.100	4.03	13.870	21.865	1.510	2.63	0.27	0.000	8.49	0.00	2.72	8.115	0.42
ERR675522	0.00	4.085	0.36	1.03	0.76	0.17	0.15	0.00	153.425	32.31	0.00	0.00	12.66	0.00	0.00	97.475	4.23	0.73	0.48	5.770	0.00	0.00	0.60	17.020	0.000	7.890	18.02	0.170	0.00	0.430	0.170	0.930	32.760	0.00	0.08	1.000	2.51	84.49	2.93	151.405	0.81	0.480	0.15	1.26	27.68	2.48	0.67	0.00	43.89	0.00	5.550	0.61	1.470	16.600	11.800	4.89	0.00	0.000	0.92	0.00	22.82	4.380	5.41
ERR675523	3.86	2.210	0.00	4.09	10.98	3.39	6.73	45.69	1.535	5.92	0.09	4.61	0.00	0.00	0.50	159.730	1.21	8.75	11.07	17.545	0.17	0.00	47.35	9.090	17.585	7.760	0.00	3.365	0.00	64.560	3.010	12.070	1.630	0.00	0.00	8.995	19.44	33.79	0.00	37.745	7.07	6.430	7.18	1.31	23.13	3.63	9.60	21.81	5.18	20.61	3.810	1.95	19.785	18.175	5.830	3.17	0.23	0.000	5.55	0.00	2.78	6.880	0.39

# calculate relative abundance
rel_ab <- sweep(D, 1, rowSums(D),`/`)

# get most abundant genomes
counts_per_genome <- data.frame(sums = colSums(rel_ab)) %>%
  rownames_to_column(var = "Sample") %>%
  left_join(Tax, by = c("Sample" = "user_genome")) %>%
  arrange(desc(sums))
  

ggplot(counts_per_genome %>%
         top_n(sums, n = 10), aes(x = reorder(Labels, sums), y = sums)) +
  geom_col() +
  labs(x = "", y = "Abundance [rel_ab]", title = "Most abundant genomes") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90))

Typical bar chart

level <- 'family'

grouped_data <- rel_ab %>%
  t() %>%
  as.data.frame() %>%
  rownames_to_column(var = "user_genome") %>%
  pivot_longer(cols = -user_genome, names_to = "Sample", values_to = "rel_ab") %>%
  left_join(Tax, by = "user_genome") %>%
  group_by(Sample, family) %>%
  summarise(summarized_rel_ab = sum(rel_ab))

ggplot(grouped_data, aes(x = Sample, y = summarized_rel_ab, fill = family)) +
  geom_col() +
  theme_minimal() + 
  theme(axis.text.x = element_text(angle = 90))

Functional annotation

Relative abundance of functional annotations per sample

The abundance is calculated as the sum of the relative abundance of all bacteria containing a function.

CAZy – Carbohydrate active enzymes

CAZy_annotations_genome <- read_tsv('Example/Results/annotations/CAZy.tsv')
CAZy_presence <- CAZy_annotations_genome %>%
  column_to_rownames("MAG")
CAZy_presence[CAZy_presence > 0] <- 1
kable(head(CAZy_presence))

	AA10	CBM15	CBM20	CBM42	CBM48	CBM50	CBM6	CBM73	CE1	CE10	GH101	GH102	GH103	GH105	GH121	GH13	GH130	GH15	GH16	GH18	GH19	GH20	GH23	GH26	GH27	GH28	GH29	GH3	GH30	GH31	GH32	GH33	GH37	GH38	GH39	GH4	GH43	GH5	GH51	GH54	GH57	GH59	GH6	GH63	GH65	GH66	GH68	GH73	GH77	GH79	GH8	GH82	GH84	GH88	GH9	GH94	GH95	GH97	GT1	GT10	GT11	GT17	GT19	GT2	GT20	GT21	GT22	GT25	GT26	GT28	GT3	GT30	GT35	GT36	GT39	GT4	GT5	GT51	GT56	GT6	GT66	GT8	GT81	GT84	GT9	PL11	PL12	PL13	PL21	PL8
MAG01	0	0	0	0	1	1	0	0	1	0	1	0	0	1	0	1	0	0	0	1	0	1	1	1	0	0	1	1	0	1	1	1	0	1	0	0	1	0	1	0	0	0	1	0	0	1	0	0	1	0	0	0	0	1	0	1	1	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1	1	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	1
MAG02	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	1	0	0	0	0	1	1	1	1	1	0	0	1	0	1	0	0	0	0	0	0	1	0	0	0	0	0
MAG03	0	0	0	0	1	0	0	0	1	0	1	0	0	1	0	1	0	0	0	0	0	1	1	1	0	0	1	1	0	1	1	1	0	0	0	0	1	1	0	0	0	0	0	1	1	0	0	0	1	0	0	0	0	0	1	1	0	0	1	0	0	1	0	1	0	0	1	0	1	1	0	0	1	1	1	1	1	1	0	0	0	1	1	0	0	1	0	0	0	0
MAG04	0	0	0	0	1	0	1	0	1	1	0	0	0	1	0	1	0	0	0	1	0	1	1	1	0	1	1	1	1	1	1	1	0	1	0	0	1	1	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	1	0	0	0	1	0	0	1	0	1	0	0	0	0	1	1	0	0	1	0	0	1	1	1	0	0	0	1	0	0	0	0	0	0	0	0
MAG05	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	1	1	0	1	1	1	0	1	0	0	1	1	1	0	0	0	1	0	0	0	0	0	1	0	0	0	1	0	1	0	1	0	1	0	0	0	0	1	0	0	0	0	1	1	0	0	1	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	1
MAG06	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	1	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	0	0	1	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0

CAZy_rel_ab <- as.matrix(rel_ab) %*% as.matrix(CAZy_presence)
kable(head(CAZy_rel_ab))

	AA10	CBM15	CBM20	CBM42	CBM48	CBM50	CBM6	CBM73	CE1	CE10	GH101	GH102	GH103	GH105	GH121	GH13	GH130	GH15	GH16	GH18	GH19	GH20	GH23	GH26	GH27	GH28	GH29	GH3	GH30	GH31	GH32	GH33	GH37	GH38	GH39	GH4	GH43	GH5	GH51	GH54	GH57	GH59	GH6	GH63	GH65	GH66	GH68	GH73	GH77	GH79	GH8	GH82	GH84	GH88	GH9	GH94	GH95	GH97	GT1	GT10	GT11	GT17	GT19	GT2	GT20	GT21	GT22	GT25	GT26	GT28	GT3	GT30	GT35	GT36	GT39	GT4	GT5	GT51	GT56	GT6	GT66	GT8	GT81	GT84	GT9	PL11	PL12	PL13	PL21	PL8
ERR675518	0.0012643	0.0011379	0.3031266	0.1543316	0.9423574	0.1360306	0.0574845	0.0012643	0.2191756	0.0782721	0.4066008	0.0084499	0.0439879	0.1185882	0.0948558	0.9852232	0.0767654	0.0188911	0.1903753	0.3946951	0.0168524	0.5855130	0.8450678	0.5196470	0.2023021	0.0195496	0.7598209	0.7303991	0.6302911	0.7941841	0.7172817	0.3483472	0.0364283	0.4016594	0.0028131	0.0428763	0.4474806	0.9122455	0.1114974	0.1543316	0.7191202	0.0336415	0.3089635	0.0620624	0.5758356	0.4229264	0.1572922	0.0001896	0.9210062	0.0001475	0.0420071	0.0007112	0.2080759	0.1241565	0.9122455	0.0578059	0.5670328	0.5285026	0.2121902	0.1268326	0.0040985	0.0166627	0.7481522	1	0.0284894	0.0115159	0.0068168	0.0578849	0.7356302	0.9950481	0.5334176	0.6749901	0.9462558	0.0782774	0.1546372	0.9915501	0.8056368	0.9991360	0.0351640	0.0124009	0.0063269	0.0949559	0.0068168	0.0203556	0.6430344	0.0907678	0.0556196	0.0333149	0.0361280	0.2342526
ERR675519	0.0099955	0.0071900	0.0073721	0.0000000	0.8548648	0.2180490	0.0847189	0.0099955	0.2410156	0.1874795	0.6622235	0.0031699	0.0582606	0.2741781	0.0606835	0.9933323	0.0613029	0.0099469	0.1869451	0.4216270	0.0308853	0.8033326	0.6999769	0.4026197	0.1332269	0.0301869	0.6597581	0.6583249	0.3468429	0.7751133	0.6207112	0.3975370	0.1741744	0.4357761	0.0011659	0.0865468	0.3233783	0.7537438	0.2929363	0.0000000	0.3428592	0.0078701	0.3902012	0.0892430	0.3521928	0.0354944	0.2996709	0.0634101	0.8104376	0.0000000	0.0643149	0.0165964	0.2609944	0.1213853	0.7537438	0.1933699	0.4565930	0.1399917	0.3899401	0.1714600	0.0125824	0.1084446	0.3838007	1	0.0118051	0.0059390	0.0000000	0.0678128	0.5847250	0.9911583	0.1508253	0.3286008	0.9053038	0.2270486	0.2682391	0.9828510	0.6794212	0.9613539	0.0515928	0.0120966	0.0034978	0.1212213	0.0000000	0.0000000	0.3408188	0.0327921	0.0007530	0.0004980	0.0016639	0.1101085
ERR675520	0.0012764	0.0124793	0.3625563	0.2441963	0.9404411	0.1251638	0.1311756	0.0012764	0.1965012	0.0553194	0.3783976	0.0353012	0.0595589	0.1522024	0.1243148	0.9602086	0.1283948	0.0364693	0.2393356	0.5828537	0.0222406	0.5257508	0.9224970	0.5901989	0.3547211	0.0197105	0.8677930	0.8345946	0.6966551	0.8772979	0.7267822	0.4066386	0.1004844	0.4689099	0.0036697	0.0233461	0.5150949	0.8906092	0.2020799	0.2441963	0.7980056	0.0899311	0.3375577	0.1382529	0.6829905	0.5120919	0.0698786	0.0005698	0.9196137	0.0000912	0.0929512	0.0000000	0.2312155	0.1475070	0.8906092	0.0603396	0.6998006	0.5876004	0.1019773	0.1215169	0.0039774	0.0252664	0.7837939	1	0.0399966	0.0050544	0.0029403	0.0325944	0.7779019	0.9922389	0.6284860	0.7953046	0.9220411	0.0520770	0.1448687	0.9543279	0.8087014	0.9985754	0.0197675	0.0306570	0.0044903	0.1037096	0.0029403	0.0187931	0.6767850	0.1466123	0.1016468	0.0891789	0.0928486	0.2090603
ERR675521	0.0102133	0.0062096	0.0077355	0.0000000	0.8671134	0.1792772	0.1255137	0.0102133	0.3014670	0.2819922	0.6659364	0.0020699	0.0412990	0.3365414	0.0229498	0.9917357	0.0671799	0.0265758	0.1602028	0.5671119	0.0326419	0.7734257	0.7063441	0.3931302	0.1808636	0.0306475	0.6847163	0.7057775	0.3244319	0.8043225	0.5989983	0.3847374	0.2139285	0.4678643	0.0039735	0.0702771	0.4170016	0.7547592	0.3982142	0.0000000	0.2834502	0.0073125	0.3043150	0.1652414	0.3598009	0.0744017	0.3021469	0.0931740	0.8147247	0.0316976	0.0673460	0.0417825	0.1684671	0.2007161	0.7547592	0.2927117	0.4637699	0.1103674	0.3063093	0.1109642	0.0139300	0.1480253	0.2776259	1	0.0407476	0.0190216	0.0000000	0.0220131	0.6425636	0.9771862	0.1368753	0.2515033	0.8997855	0.2206896	0.3194537	0.9334774	0.7545174	0.9028072	0.0330347	0.0168157	0.0061945	0.1290340	0.0000000	0.0000000	0.2711594	0.1055781	0.0016317	0.0012238	0.0051973	0.0742431
ERR675522	0.0014887	0.0239595	0.1960975	0.0001018	0.9580486	0.1299576	0.1060681	0.0014887	0.2724231	0.1633520	0.4828034	0.0137675	0.0382232	0.1210317	0.0910664	0.9791706	0.0971740	0.0361873	0.2122953	0.2317186	0.0134875	0.6709165	0.7911148	0.4718034	0.0679340	0.0225344	0.7129824	0.7333282	0.6393607	0.7622438	0.6609408	0.4023807	0.0580092	0.3467000	0.0062221	0.0586327	0.3363171	0.9122419	0.1605655	0.0001018	0.6385273	0.0566223	0.3730007	0.0933313	0.4975252	0.2942385	0.2125116	0.0004072	0.9498798	0.0001909	0.0728328	0.0013997	0.1726215	0.1168073	0.9122419	0.0576529	0.4454390	0.4628838	0.2725567	0.1302503	0.0008525	0.0261735	0.6686707	1	0.0568767	0.0216564	0.0004581	0.0078253	0.6823746	0.9896426	0.4631828	0.5611648	0.9451209	0.1554885	0.1611826	0.9633037	0.8358845	0.9985367	0.0211220	0.0000000	0.0070619	0.0787368	0.0004581	0.0229288	0.4979260	0.1164892	0.0558461	0.0558461	0.0620682	0.2889453
ERR675523	0.0169554	0.0096986	0.0047807	0.0000000	0.8963949	0.3147592	0.0817249	0.0169554	0.2407301	0.1627776	0.6044350	0.0106452	0.0408041	0.2155301	0.0388356	0.9841283	0.0193561	0.0467920	0.2900599	0.4855824	0.0262288	0.7348313	0.7743254	0.4116219	0.1596293	0.0287049	0.7088015	0.7201394	0.4945197	0.8741649	0.5236292	0.5053089	0.1900970	0.3231614	0.0043486	0.1117467	0.2680563	0.7242548	0.2619655	0.0000000	0.4484958	0.0097809	0.4299491	0.0803119	0.4414654	0.0428618	0.1786356	0.0140061	0.8775190	0.0098495	0.0161049	0.0211737	0.3184356	0.1492380	0.7242548	0.1687518	0.6237362	0.1366723	0.3342936	0.2234660	0.0131693	0.1044830	0.4128771	1	0.0441993	0.0124696	0.0000000	0.0302687	0.7762048	0.9705269	0.1765505	0.3964429	0.9184329	0.1100800	0.3158155	0.9610821	0.6738618	0.9433722	0.0249324	0.0241231	0.0052266	0.1028712	0.0000000	0.0000000	0.4028904	0.1029260	0.0074214	0.0071059	0.0114545	0.0978298

pheatmap(CAZy_rel_ab)

KEGG orthologs

Kegg_annotations_genome <- read_tsv('Example/Results/annotations/KO.tsv')
Kegg_presence <- Kegg_annotations_genome %>%
  column_to_rownames("MAG")
Kegg_presence[Kegg_presence > 0] <- 1
kable(head(Kegg_presence))

	K00001	K00002	K00003	K00004	K00005	K00008	K00009	K00010	K00012	K00013	K00014	K00015	K00016	K00018	K00020	K00021	K00024	K00027	K00029	K00030	K00031	K00033	K00036	K00040	K00041	K00042	K00043	K00045	K00048	K00052	K00053	K00054	K00057	K00058	K00059	K00060	K00066	K00067	K00073	K00074	K00075	K00077	K00082	K00086	K00087	K00088	K00091	K00094	K00096	K00097	K00098	K00099	K00100	K00102	K00104	K00111	K00112	K00113	K00123	K00124	K00125	K00127	K00128	K00133	K00134	K00135	K00140	K00145	K00147	K00150	K00156	K00158	K00161	K00162	K00164	K00166	K00169	K00170	K00171	K00172	K00174	K00175	K00176	K00177	K00179	K00180	K00184	K00185	K00186	K00187	K00193	K00194	K00196	K00197	K00198	K00202	K00207	K00208	K00210	K00211	K00214	K00215	K00217	K00219	K00220	K00223	K00226	K00230	K00231	K00232	K00239	K00240	K00241	K00242	K00243	K00244	K00245	K00246	K00247	K00248	K00249	K00254	K00259	K00260	K00261	K00262	K00265	K00266	K00273	K00275	K00278	K00281	K00282	K00283	K00284	K00285	K00286	K00287	K00288	K00290	K00294	K00297	K00298	K00302	K00303	K00305	K00311	K00313	K00314	K00315	K00317	K00318	K00329	K00330	K00331	K00332	K00333	K00334	K00335	K00336	K00337	K00338	K00339	K00340	K00341	K00342	K00343	K00344	K00346	K00347	K00348	K00349	K00350	K00351	K00354	K00355	K00356	K00362	K00364	K00366	K00370	K00371	K00373	K00374	K00375	K00380	K00381	K00382	K00383	K00384	K00385	K00390	K00392	K00395	K00404	K00405	K00412	K00425	K00426	K00428	K00432	K00433	K00436	K00437	K00441	K00450	K00459	K00483	K00484	K00491	K00496	K00505	K00507	K00520	K00525	K00526	K00528	K00532	K00533	K00534	K00537	K00543	K00547	K00548	K00549	K00554	K00556	K00557	K00558	K00560	K00561	K00563	K00564	K00566	K00567	K00568	K00569	K00571	K00573	K00574	K00575	K00588	K00590	K00595	K00598	K00600	K00602	K00603	K00604	K00605	K00606	K00608	K00609	K00610	K00611	K00612	K00615	K00616	K00619	K00620	K00622	K00625	K00626	K00627	K00630	K00631	K00633	K00634	K00638	K00639	K00640	K00641	K00645	K00647	K00648	K00651	K00652	K00655	K00656	K00657	K00658	K00661	K00662	K00663	K00666	K00674	K00675	K00677	K00681	K00683	K00684	K00687	K00688	K00689	K00690	K00691	K00692	K00694	K00697	K00700	K00701	K00702	K00703	K00705	K00712	K00713	K00720	K00721	K00728	K00737	K00743	K00745	K00748	K00754	K00756	K00757	K00758	K00759	K00760	K00761	K00762	K00763	K00764	K00765	K00766	K00767	K00768	K00769	K00772	K00773	K00782	K00783	K00784	K00786	K00788	K00789	K00790	K00791	K00793	K00794	K00795	K00796	K00797	K00798	K00799	K00800	K00801	K00803	K00805	K00806	K00809	K00812	K00813	K00814	K00817	K00819	K00820	K00821	K00823	K00824	K00826	K00831	K00832	K00833	K00835	K00841	K00842	K00844	K00845	K00847	K00848	K00849	K00850	K00851	K00852	K00853	K00854	K00855	K00856	K00857	K00858	K00859	K00860	K00863	K00864	K00865	K00867	K00868	K00869	K00872	K00873	K00874	K00876	K00878	K00879	K00880	K00881	K00882	K00883	K00884	K00887	K00891	K00893	K00895	K00897	K00899	K00901	K00912	K00917	K00918	K00919	K00925	K00926	K00927	K00928	K00929	K00930	K00931	K00933	K00934	K00936	K00937	K00938	K00939	K00940	K00941	K00942	K00943	K00945	K00946	K00948	K00949	K00950	K00951	K00954	K00955	K00956	K00957	K00958	K00962	K00963	K00965	K00966	K00968	K00969	K00970	K00971	K00972	K00973	K00974	K00975	K00978	K00979	K00980	K00981	K00982	K00983	K00984	K00986	K00988	K00989	K00990	K00991	K00992	K00995	K00996	K00997	K00998	K00999	K01000	K01002	K01003	K01004	K01006	K01007	K01008	K01011	K01012	K01014	K01023	K01026	K01027	K01028	K01029	K01031	K01034	K01035	K01039	K01040	K01042	K01046	K01048	K01051	K01053	K01056	K01057	K01058	K01060	K01061	K01066	K01067	K01069	K01070	K01071	K01073	K01075	K01077	K01079	K01081	K01082	K01083	K01085	K01087	K01089	K01090	K01091	K01092	K01093	K01095	K01096	K01101	K01104	K01113	K01117	K01118	K01119	K01120	K01121	K01126	K01128	K01129	K01130	K01133	K01134	K01135	K01136	K01137	K01138	K01139	K01141	K01142	K01143	K01144	K01146	K01147	K01150	K01151	K01153	K01154	K01155	K01156	K01159	K01163	K01167	K01173	K01174	K01175	K01176	K01178	K01179	K01181	K01182	K01183	K01185	K01186	K01187	K01190	K01191	K01192	K01193	K01194	K01195	K01197	K01198	K01200	K01201	K01202	K01205	K01206	K01207	K01208	K01209	K01210	K01212	K01213	K01214	K01215	K01216	K01218	K01219	K01220	K01222	K01223	K01224	K01226	K01227	K01232	K01233	K01235	K01236	K01239	K01241	K01243	K01244	K01246	K01247	K01250	K01251	K01253	K01255	K01256	K01258	K01259	K01261	K01262	K01263	K01265	K01266	K01267	K01269	K01270	K01271	K01273	K01274	K01277	K01278	K01281	K01283	K01284	K01286	K01295	K01297	K01299	K01303	K01304	K01305	K01308	K01317	K01318	K01322	K01337	K01338	K01342	K01347	K01356	K01358	K01361	K01365	K01371	K01372	K01387	K01399	K01406	K01409	K01414	K01415	K01416	K01419	K01420	K01421	K01424	K01425	K01426	K01428	K01429	K01430	K01433	K01434	K01436	K01438	K01439	K01442	K01443	K01444	K01447	K01448	K01449	K01450	K01452	K01457	K01459	K01460	K01462	K01463	K01464	K01465	K01466	K01467	K01468	K01469	K01470	K01476	K01478	K01480	K01481	K01483	K01485	K01486	K01487	K01488	K01489	K01491	K01492	K01493	K01494	K01495	K01496	K01497	K01498	K01501	K01502	K01507	K01512	K01515	K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0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	1	0	0	0	1	0	1	1	1	1	1	0	1	1	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	0	1	1	0	0	0	1	0	1	1	1	0	1	0	1	1	0	1	1	1	0	0	0	0	0	0	0	1	1	0	0	1	0	0	1	0	0	1	1	1	1	0	1	1	0	0	1	0	1	0	1	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1	1	1	1	1	1	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0	1	0	1	0	1	1	0	1	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	1	1	1	0	0	1	0	0	1	1	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	0	1	0	1	1	0	1	1	0	0	0	1	0	0	0	1	1	1	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	1	0	0	1	1	1	1	0	0	1	1	0	1	1	0	1	0	1	0	1	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	1	1	0	0	0	0	0	0	0	1	0	1	0	0	1	1	1	0	1	1	0	1	1	0	0	0	0	1	0	0	1	0	0	0	0	1	1	0	0	0	0	0	1	1	0	0	0	1	1	0	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	1	1	1	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	1	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1	1	0	0	0	0	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	1	1	0	0	1	1	0	0	0	1	1	0	0	1	1	0	0	0	0	1	1	0	1	0	0	0	0	0	1	0	0	1	0	0	1	1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	1	1	1	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	1	1	1	0	0	0	0	0	0	0	0	1	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	1	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	1	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	1	1	1	0	1	1	0	0	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	1	1	1	1	1	0	0	0	0	0	0	0	1	1	0	0	1	0	0	0	0	1	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0	1	0	0	0	1	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	1	1	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	1	1	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0	1	0	0	1	0	1	0	1	0	0	0	0	1	1	1	0	0	0	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	1	1	1	1	1	0	0	1	0	0	1	0	0	0	0	0	1	1	0	1	0	0	0	0	0	0	0	0	1	1	0	1	0	0	0	0	1	0	1	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	1	0	1	1	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	1	1	1	0	1	0	1	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0	0	0	0	0	1	1	0	0	1	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	1	1	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	1	1	0	0	0	0	1	1	0	1	0	0	0	0	0	0	0	1	1	1	1	0	1	0	0	0	0	1	0	1	1	0	0	0	1	1	1	1	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	1	0	0	1	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	1	1	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	1	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	1	1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	1	1	1	0	1	0	0	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	0	1	1	1	1	0	0	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	1	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	1	0	1	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1

Kegg_rel_ab <- as.matrix(rel_ab) %*% as.matrix(Kegg_presence)
kable(head(Kegg_rel_ab))

	K00001	K00002	K00003	K00004	K00005	K00008	K00009	K00010	K00012	K00013	K00014	K00015	K00016	K00018	K00020	K00021	K00024	K00027	K00029	K00030	K00031	K00033	K00036	K00040	K00041	K00042	K00043	K00045	K00048	K00052	K00053	K00054	K00057	K00058	K00059	K00060	K00066	K00067	K00073	K00074	K00075	K00077	K00082	K00086	K00087	K00088	K00091	K00094	K00096	K00097	K00098	K00099	K00100	K00102	K00104	K00111	K00112	K00113	K00123	K00124	K00125	K00127	K00128	K00133	K00134	K00135	K00140	K00145	K00147	K00150	K00156	K00158	K00161	K00162	K00164	K00166	K00169	K00170	K00171	K00172	K00174	K00175	K00176	K00177	K00179	K00180	K00184	K00185	K00186	K00187	K00193	K00194	K00196	K00197	K00198	K00202	K00207	K00208	K00210	K00211	K00214	K00215	K00217	K00219	K00220	K00223	K00226	K00230	K00231	K00232	K00239	K00240	K00241	K00242	K00243	K00244	K00245	K00246	K00247	K00248	K00249	K00254	K00259	K00260	K00261	K00262	K00265	K00266	K00273	K00275	K00278	K00281	K00282	K00283	K00284	K00285	K00286	K00287	K00288	K00290	K00294	K00297	K00298	K00302	K00303	K00305	K00311	K00313	K00314	K00315	K00317	K00318	K00329	K00330	K00331	K00332	K00333	K00334	K00335	K00336	K00337	K00338	K00339	K00340	K00341	K00342	K00343	K00344	K00346	K00347	K00348	K00349	K00350	K00351	K00354	K00355	K00356	K00362	K00364	K00366	K00370	K00371	K00373	K00374	K00375	K00380	K00381	K00382	K00383	K00384	K00385	K00390	K00392	K00395	K00404	K00405	K00412	K00425	K00426	K00428	K00432	K00433	K00436	K00437	K00441	K00450	K00459	K00483	K00484	K00491	K00496	K00505	K00507	K00520	K00525	K00526	K00528	K00532	K00533	K00534	K00537	K00543	K00547	K00548	K00549	K00554	K00556	K00557	K00558	K00560	K00561	K00563	K00564	K00566	K00567	K00568	K00569	K00571	K00573	K00574	K00575	K00588	K00590	K00595	K00598	K00600	K00602	K00603	K00604	K00605	K00606	K00608	K00609	K00610	K00611	K00612	K00615	K00616	K00619	K00620	K00622	K00625	K00626	K00627	K00630	K00631	K00633	K00634	K00638	K00639	K00640	K00641	K00645	K00647	K00648	K00651	K00652	K00655	K00656	K00657	K00658	K00661	K00662	K00663	K00666	K00674	K00675	K00677	K00681	K00683	K00684	K00687	K00688	K00689	K00690	K00691	K00692	K00694	K00697	K00700	K00701	K00702	K00703	K00705	K00712	K00713	K00720	K00721	K00728	K00737	K00743	K00745	K00748	K00754	K00756	K00757	K00758	K00759	K00760	K00761	K00762	K00763	K00764	K00765	K00766	K00767	K00768	K00769	K00772	K00773	K00782	K00783	K00784	K00786	K00788	K00789	K00790	K00791	K00793	K00794	K00795	K00796	K00797	K00798	K00799	K00800	K00801	K00803	K00805	K00806	K00809	K00812	K00813	K00814	K00817	K00819	K00820	K00821	K00823	K00824	K00826	K00831	K00832	K00833	K00835	K00841	K00842	K00844	K00845	K00847	K00848	K00849	K00850	K00851	K00852	K00853	K00854	K00855	K00856	K00857	K00858	K00859	K00860	K00863	K00864	K00865	K00867	K00868	K00869	K00872	K00873	K00874	K00876	K00878	K00879	K00880	K00881	K00882	K00883	K00884	K00887	K00891	K00893	K00895	K00897	K00899	K00901	K00912	K00917	K00918	K00919	K00925	K00926	K00927	K00928	K00929	K00930	K00931	K00933	K00934	K00936	K00937	K00938	K00939	K00940	K00941	K00942	K00943	K00945	K00946	K00948	K00949	K00950	K00951	K00954	K00955	K00956	K00957	K00958	K00962	K00963	K00965	K00966	K00968	K00969	K00970	K00971	K00972	K00973	K00974	K00975	K00978	K00979	K00980	K00981	K00982	K00983	K00984	K00986	K00988	K00989	K00990	K00991	K00992	K00995	K00996	K00997	K00998	K00999	K01000	K01002	K01003	K01004	K01006	K01007	K01008	K01011	K01012	K01014	K01023	K01026	K01027	K01028	K01029	K01031	K01034	K01035	K01039	K01040	K01042	K01046	K01048	K01051	K01053	K01056	K01057	K01058	K01060	K01061	K01066	K01067	K01069	K01070	K01071	K01073	K01075	K01077	K01079	K01081	K01082	K01083	K01085	K01087	K01089	K01090	K01091	K01092	K01093	K01095	K01096	K01101	K01104	K01113	K01117	K01118	K01119	K01120	K01121	K01126	K01128	K01129	K01130	K01133	K01134	K01135	K01136	K01137	K01138	K01139	K01141	K01142	K01143	K01144	K01146	K01147	K01150	K01151	K01153	K01154	K01155	K01156	K01159	K01163	K01167	K01173	K01174	K01175	K01176	K01178	K01179	K01181	K01182	K01183	K01185	K01186	K01187	K01190	K01191	K01192	K01193	K01194	K01195	K01197	K01198	K01200	K01201	K01202	K01205	K01206	K01207	K01208	K01209	K01210	K01212	K01213	K01214	K01215	K01216	K01218	K01219	K01220	K01222	K01223	K01224	K01226	K01227	K01232	K01233	K01235	K01236	K01239	K01241	K01243	K01244	K01246	K01247	K01250	K01251	K01253	K01255	K01256	K01258	K01259	K01261	K01262	K01263	K01265	K01266	K01267	K01269	K01270	K01271	K01273	K01274	K01277	K01278	K01281	K01283	K01284	K01286	K01295	K01297	K01299	K01303	K01304	K01305	K01308	K01317	K01318	K01322	K01337	K01338	K01342	K01347	K01356	K01358	K01361	K01365	K01371	K01372	K01387	K01399	K01406	K01409	K01414	K01415	K01416	K01419	K01420	K01421	K01424	K01425	K01426	K01428	K01429	K01430	K01433	K01434	K01436	K01438	K01439	K01442	K01443	K01444	K01447	K01448	K01449	K01450	K01452	K01457	K01459	K01460	K01462	K01463	K01464	K01465	K01466	K01467	K01468	K01469	K01470	K01476	K01478	K01480	K01481	K01483	K01485	K01486	K01487	K01488	K01489	K01491	K01492	K01493	K01494	K01495	K01496	K01497	K01498	K01501	K01502	K01507	K01512	K01515	K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pheatmap(Kegg_rel_ab)